深度卷积神经网络已广泛应用于医学图像分析，例如全切片图像中的病灶识别、癌症检测和细胞分割等。然而，研究人员往往不可避免地会尽力细化注释以增强模型性能，特别是细胞分割任务。弱监督学习可以大大减少标注的工作量，但弱监督学习方法和全监督学习方法之间仍然存在巨大的性能差距。在这项工作中，我们提出了一种弱监督细胞分割方法，即多任务细胞分割网络（MTCSNet），适用于多模态医学图像，包括病理、明场、荧光、相位衬度和微分干涉衬度图像。 MTCSNet以单阶段训练方式学习，每个单元只有两个注释点提供监督信息，第一个是质心，第二个是其边界。此外，还精心设计了五个辅助任务来训练网络，包括两个像素级分类、一个像素级回归、一个局部温度缩放和一个实例级距离回归任务，该任务旨在回归细胞质心之间的距离及其八个方向的边界。实验结果表明，我们的方法在公共多模态医学图像数据集上优于所有最先进的弱监督细胞分割方法。令人鼓舞的性能还表明，采用两点标记方法的单阶段学习足以进行细胞分割，而不是精细轮廓描绘。代码位于：https://github.com/binging512/MTCSNet。

Deep convolution neural networks have been widely used in medical image analysis, such as lesion identification in whole-slide images, cancer detection, and cell segmentation, etc. However, it is often inevitable that researchers try their best to refine annotations so as to enhance the model performance, especially for cell segmentation task. Weakly supervised learning can greatly reduce the workload of annotations, while there is still a huge performance gap between the weakly and fully supervised learning approaches. In this work, we propose a weakly-supervised cell segmentation method, namely Multi-Task Cell Segmentation Network (MTCSNet), for multi-modal medical images, including pathological, brightfield, fluorescent, phase-contrast and differential interference contrast images. MTCSNet is learnt in a single-stage training manner, where only two annotated points for each cell provide supervision information, and the first one is the centroid, the second one is its boundary. Additionally, five auxiliary tasks are elaborately designed to train the network, including two pixel-level classifications, a pixel-level regression, a local temperature scaling and an instance-level distance regression task, which is proposed to regress the distances between the cell centroid and its boundaries in eight orientations. The experimental results indicate that our method outperforms all state-of-the-art weakly-supervised cell segmentation approaches on public multi-modal medical image datasets. The promising performance also shows that a single-stage learning with two-point labeling approach are sufficient for cell segmentation, instead of fine contour delineation. The codes are available at: https://github.com/binging512/MTCSNet.